Grafit: Learning fine-grained image representations with coarse labels
Hugo Touvron∗,† Alexandre Sablayrolles ∗ Matthijs Douze ∗ Matthieu Cord † Hervé Jégou ∗ ∗ Facebook AI Research †Sorbonne University Figure 1: Category-level retrieval orders images based on their semantic similarity to a query. The Grafit method, although it has used only coarse labels (like ’pyrgus’) at training time, produces a ranking consistent with fine-grained labels. Unsupervised learning is a particular case of this task, in which the set of coarse labels is reduced to a singleton. Image credit: [1].
Abstract
This paper tackles the problem of learning a finer representation than the one provided by training labels. This enables fine-grained category retrieval of images in a collection annotated with coarse labels only.
原文 arXiv:2011.12982;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2011.12982v1